Minimum RAM and VRAM Requirements for Running Llama 4
Quick Takeaways: The Memory Cheat Sheet
- The Golden Ratio: For fast local inference, Video RAM (VRAM) is infinitely more valuable than System RAM.
- Entry Level (8B Models): Requires 6GB - 8GB VRAM. Ideally, an NVIDIA RTX 4060 or 5060.
- The "Sweet Spot" (70B Models): Requires 48GB+ of Memory.
- This forces a choice: Dual RTX 4090s (Desktop) or a MacBook Pro M4 Max (64GB/128GB).
- Quantization is Mandatory: You will likely run models at 4-bit quantization (Q4_K_M). Running full precision (FP16) on a laptop is mathematically impossible for large models.
- System RAM Rule: Always have double your VRAM in System RAM to prevent OS bottlenecks (e.g., if you have 16GB VRAM, get 32GB System RAM).
Understanding the minimum RAM and VRAM requirements for running Llama 4 is the difference between a functional AI agent and a laptop that crashes instantly.
In 2026, model parameters equate directly to gigabytes. If you don't have the space, the model simply won't load.
This deep dive is part of our extensive guide on Best AI Laptop 2026.
While marketing materials focus on "TOPS" (Trillions of Operations Per Second), the real bottleneck for local LLMs is capacity.
This guide breaks down exactly how many gigabytes you need for every size of Llama 4, preventing you from buying expensive hardware that can't handle your workload.
The "VRAM vs. System RAM" Confusion
Before looking at the numbers, you must understand the architecture.
On Windows/Linux (NVIDIA GPUs): The model lives in your VRAM (Video RAM).
If you have 64GB of System RAM (DDR5) but only 8GB of VRAM (RTX 4060), you cannot fast-load a 70B model.
It will offload to your CPU, running at 1 token per second, too slow to be usable.
On Mac (Apple Silicon): The M4 chips use Unified Memory. The CPU and GPU share the same pool.
If you buy a MacBook with 64GB RAM, your GPU effectively has ~48GB-54GB available for AI.
For a detailed comparison of these architectures, see our breakdown of MacBook Pro M4 Max vs Windows for Local LLMs.
Spec Breakdown: Llama 4 (8B Parameter Model)
This is the standard "coding assistant" or "chatbot" size. It is efficient, fast, and runs on most modern hardware.
Minimum VRAM: 6GB (Runs slowly/tight fit)
Recommended VRAM: 8GB (Runs comfortably at Q4_K_M)
Ideal Hardware: NVIDIA RTX 4060 / RTX 5060 Laptop.
Context Window: At 8GB VRAM, you might struggle with long context windows (large PDF uploads).
If you are on a strict budget, this is the target tier.
Check our recommendations for Best Budget AI Laptops Under 1000 Dollars to find machines that hit this 8GB minimum without overspending.
Spec Breakdown: Llama 4 (70B Parameter Model)
This is the "GPT-4 class" intelligence. It creates complex agents, writes entire software modules, and reasons deeply.
Full Precision (FP16): Requires ~140GB VRAM (Impossible on laptops).
4-Bit Quantized (Q4_K_M): Requires ~40GB - 48GB VRAM.
The Hardware Problem: No consumer NVIDIA laptop GPU has 48GB of VRAM. The RTX 5090 tops out at 24GB.
The Solution: You must use a MacBook Pro M4 Max with 64GB or 128GB of Unified Memory, or build a desktop with dual GPUs.
The Role of Quantization (GGUF)
You will rarely run models at their "original" size. You will use Quantization, compressing the model weights from 16-bit floating point numbers down to 4-bit integers.
The Math of Memory:
Q4 (4-bit): High quality, low VRAM. (Standard usage).
Q8 (8-bit): Higher precision, double the VRAM. (Diminishing returns).
FP16 (16-bit): Raw model. Massive VRAM. (Research use only).
Recommendation: Always plan your hardware purchases based on 4-bit (Q4_K_M) requirements.
It allows you to punch above your weight class with consumer hardware.
Conclusion
When calculating the Minimum RAM and VRAM Requirements for Running Llama 4, always err on the side of VRAM capacity.
For casual use and learning, 8GB VRAM is the floor.
For serious development and agentic workflows, 24GB VRAM (RTX 5090) or 64GB Unified Memory (Mac) is the standard.
Don't let a lack of memory be the reason your code stops compiling.
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Frequently Asked Questions (FAQ)
Only if you are using the smallest 8B model heavily quantized. Even then, the Operating System will fight for memory, causing slowdowns. We strongly recommend 32GB of System RAM as a baseline for any developer.
To run efficiently at 4-bit quantization, it needs roughly 5.5GB to 6GB of VRAM. This makes 8GB cards (like the RTX 3070, 4060, 5060) the perfect entry-level choice.
On Windows/Linux, it matters very little for inference, as the speed comes from the GPU VRAM. On Mac (Unified Memory), memory bandwidth is critical, and the M4 Max's high bandwidth is a major performance driver.
The model offloads layers to your system RAM (CPU). Your speed will drop from ~50 tokens per second (instant) to ~3 tokens per second (painfully slow). This is why VRAM capacity is non-negotiable.
No. A 70B model, even heavily compressed to Q2 (which makes it very stupid), still requires roughly 24GB+ of space. With 12GB, you are limited to models in the 10B-20B parameter range.